1. Technical Field
This disclosure relates to digital radiography, and more particularly to fully automatic detection of a spine axis and spine boundary in digital radiography.
2. Description of the Related Art
With the development of digital imaging technologies, the interest in using computers for assisting deformity analysis of scoliotic spines in radiography is increasing. Currently, most of these measurements are manually made. Manual measurement is not only time-consuming, but also subject to errors depending on the person""s skill, experience and other human factors.
Spine axis and spine boundaries are important anatomies that are not only components for deformity measurement but also the reference positions used for the deformity quantification from many other anatomic landmarks. Existing algorithms for detecting spine axis and spine boundary require human interaction. Usually a set of control points is needed to be placed manually on the spine axis.
Kauffmann and Guise, in xe2x80x9cDigital Radiography Segmentation of Scoliotic Vertebral Body Using Deformable Modelsxe2x80x9d, Proceedings of SPIE-Medical Imaging, Vol. 3034, pp. 243-251, 1997, used a cubic curve to interpolate a set of manually placed control points to determine the axis of the spine. A method, called xe2x80x9cactive contourxe2x80x9d, is applied to detect each vertebra. The spine boundary is found by simply connecting the boundaries of the detected vertebrae. Since the active contour method is sensitive to image noise, the success of the method depends on the image quality and the success of the detection of individual vertebrae.
Verdonck et al., in xe2x80x9cComputer Assisted Quantitative Analysis of Deformities of the Human Spinexe2x80x9d, Proceedings of Medical Image Computing and Computer Assisted Intervention, pp. 822-831, 1998, used a poly-Bezier curve for the interpolation of the spine axis from a set of manually placed control points, with the possibility of interactive editing of the interpolation. The spine boundaries are found by linking strong edges on each side of the spine axis. Since strong edges not belonging to the spine boundary may interfere with the linking process, the boundaries thus determined may appear unsmooth, noise-corrupted, and sometimes contain errors.
Therefore, a need exists for a stable detection method that requires no human interaction. A further need exists for a method where the placement of control points on the spine axis is unnecessary. A still further need exists for a method, which integrates domain-specific knowledge about the spine shape into the detection process in a systematic way, so that errors can be avoided at the very early stage of detection.
A system and method for automatically detecting a spinal axis and spinal boundaries from an image, in accordance with the present invention, includes scanning an input image in at least two scan directions to generate a ridge map associated with each scan direction. The ridge maps are fused to enhance a projected spinal axis. A piece-wise linear approximation of the projected spine axis is provided, and a gradient map of the input image is enhanced project spine boundaries. Spine boundaries are determined by constraining the projected spine boundaries relative to each other and the spine axis.
Another method for automatically detecting a spinal axis and spinal boundaries from an image, includes the steps of providing an input image of a spine, scanning the input image in at least two scan directions to generate a ridge map associated with each scan direction, fusing the ridge maps to enhance a projected spinal axis, providing a piece-wise linear approximation of the projected spine axis, enhancing a gradient map of the input image to highlight projected spine boundaries associated with the linear approximation of the projected spine axis, providing a piece-wise linear approximation of the projected spine boundaries, and constraining the projected spine boundaries relative to each other and the projected spine axis to determine spine boundaries.
In other methods, the step of providing a piece-wise linear approximation of the projected spine axis may include the steps of cutting an image of the projected spine axis into segments by employing a plurality of equally spaced cutting lines and determining endpoints of the segments to approximate the spine axis as piece-wise linear segments. The method may include the step of constraining the segments by considering angular relationships among the segments and projection strength of the segments.
In still other methods, the step of determining the spine boundaries may include the steps of cutting the gradient map of the projected spine boundaries into segments by employing a plurality of equally spaced cutting lines and defining endpoints of the segments at intersections between the cutting lines and the projected spine boundaries wherein the segments are approximated as lines. Constraining the projected spine boundaries may further include the step of constraining the segments by considering angular relationships among the segments, distances between adjacent segments and projection strength of the segments.
The step of generating an updated spine axis from the spinal boundaries may be included. The step of determining the spine boundaries may include employing a dual dynamic programming procedure for detecting the spinal boundaries. The method may include the step of downsizing and smoothing the input image. The above methods may be implemented by a program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform these method steps.
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.